# CRCNS: Neural Basis of Inductive Bias

> **NIH NIH R01** · DARTMOUTH COLLEGE · 2024 · $390,866

## Abstract

A hallmark of biological intelligence is the ability to learn from a remarkably small set of experiences to
select appropriate behaviors in novel contexts. This ability arises from inductive bias, referring to the
assumptions used in generalizing prior observations to novel data. In machine learning, inductive bias is
essential for efficient learning from a small number of examples as animals frequently do. In
neuroscience, inductive bias has been mostly considered as shaped by evolution to produce an innate
inductive bias. Although an agent’s inductive bias should be adaptive to changing task demands, how
brains develop inductive bias, use it to guide learning, and adapt it to task statistics, remain poorly
understood. The overall goal of this project is to study the neural and computational bases of inductive
bias adaptation by training non-human primates on a novel learning task, characterizing choice behavior
and neuronal activity, and developing computational models of learning in neural networks. Our
theoretical framework of neural kernel learning makes precise predictions for behavior and neural activity
that will be experimentally tested in the proposed studies. We will characterize behavior in a newly
designed “crosstalk” task, which is designed to characterize inductive bias and to drive a subject’s
adaptation through well-defined and variable task statistics (Aim 1). In this task, the subject learns through
experience to categorize stimuli composed of multiple features. Within a block of trials, certain features
are differentially informative of the category. Next, we will record simultaneous spiking activity from
neurons in dorsolateral prefrontal cortex and the dorsal striatum during task performance (Aim 2). In
parallel, we will develop and refine algorithmic and artificial neural network models of adaptive learning
(Aim 3) to generate testable predictions for behavior and neural activity. Results from these studies will
impact paradigms used to study the computational and neural bases of learning and generalization in
humans and animals.

## Key facts

- **NIH application ID:** 10929555
- **Project number:** 5R01MH132386-03
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** DAEYEOL LEE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $390,866
- **Award type:** 5
- **Project period:** 2022-08-12 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10929555

## Citation

> US National Institutes of Health, RePORTER application 10929555, CRCNS: Neural Basis of Inductive Bias (5R01MH132386-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10929555. Licensed CC0.

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